Metabolic Reprogramming of the Neovascular Niche Promotes Regenerative Angiogenesis in Proliferative Retinopathy
Bibliographic record
Abstract
ABSTRACT Healthy blood vessels supply neurons to preserve metabolic function. In blinding ischemic proliferative retinopathies (PRs), pathological neovascular tufts often emerge in lieu of needed physiological neuroretina revascularization. We show that metabolic shifts in the neurovascular niche define this angiogenic dichotomy between healthy and diseased blood vessel growth. Fatty acid oxidation (FAO) metabolites accumulated in human and murine retinopathy samples. Neovascular tufts with a distinct single-cell transcriptional signature highly expressed FAO enzymes. The deletion of Sirt3 , an FAO regulator, shifted the neurovascular niche metabolism from FAO to glycolysis and suppressed tuft formation. This metabolic transition increased Vegf expression in astrocytes and reprogrammed pathological EC to a physiological phenotype, hastening vascular regeneration of the ischemic retina. Our findings identify SIRT3 as a metabolic switch in the neurovascular niche, offering a new therapeutic target for optimizing ischemic tissue revascularization. Highlights Pathological EC favor FAO over glycolysis. Unique signature for pathological EC found in proliferative retinopathy model. Sirt3 deletion shifts astrocytes and EC metabolism from FAO to glycolysis. Metabolic reprogramming of the vascular niche enhances physiological revascularization. Graphical Abstract
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".